Approximate Predictive Pivots and Densities
本文提出了两种几乎适用于任何参数模型的预测似然函数，一种可解释为近似预测枢轴量，另一种近似于平坦先验下的贝叶斯预测密度，并讨论了校准和比较这些似然函数的标准。
This paper suggests two predictive likelihoods that can be applied in almost any parametric model setting. The first can sometimes be interpreted as an approximate predictive pivot (Barnard, 1986) while the second is often an approximation to a Bayesian predictive density with a flat prior. The issue of calibrating various predictive likelihood in terms of long run predictive coverage is also discussed and a specific criterion by which these likelhoods can be compared is proposed.